U.S. patent application number 10/448316 was filed with the patent office on 2004-12-02 for personalizing content using an intermediary bridge.
Invention is credited to Bill, David S..
Application Number | 20040243592 10/448316 |
Document ID | / |
Family ID | 33451463 |
Filed Date | 2004-12-02 |
United States Patent
Application |
20040243592 |
Kind Code |
A1 |
Bill, David S. |
December 2, 2004 |
Personalizing content using an intermediary bridge
Abstract
A user's access to content may be managed by determining a mood
originating point for a present track for a user, with the mood
originating point being related to a mood indicator for the present
track, identifying a mood destination for a user playlist, the mood
destination being related to a mood indicator for an end track that
is targeted for the user, and calculating a mood transition from
the mood originating point to the mood destination. The mood
transition includes one or more intermediary tracks between the
mood destination and the mood originating point, such that a
quantified mood transition between two tracks in a user playlist
including the present track, the intermediary tracks, and the end
track, is less than an identified mood transition threshold.
Inventors: |
Bill, David S.; (San
Francisco, CA) |
Correspondence
Address: |
FISH & RICHARDSON P.C.
1425 K STREET, N.W.
11TH FLOOR
WASHINGTON
DC
20005-3500
US
|
Family ID: |
33451463 |
Appl. No.: |
10/448316 |
Filed: |
May 30, 2003 |
Current U.S.
Class: |
1/1 ; 707/999.1;
707/E17.009 |
Current CPC
Class: |
G06F 16/40 20190101 |
Class at
Publication: |
707/100 |
International
Class: |
G06F 017/00 |
Claims
What is claimed is:
1. A method of managing content accessed by a user, the method
comprising: determining a mood originating point for a present
track for a user, the mood originating point being related to a
mood indicator for the present track; identifying a mood
destination for a user playlist, the mood destination being related
to a mood indicator for an end track that is targeted for the user;
and calculating a mood transition from the mood originating point
to the mood destination, the mood transition comprising one or more
intermediary tracks between the mood destination and the mood
originating point, such that a quantified mood transition between
two tracks in a user playlist comprising the present track, the
intermediary tracks, and the end track is less than an identified
mood transition threshold.
2. The method of claim 1 further comprising determining the mood
indicator for the present track or the end track by accessing a
model of user mood states.
3. The method of claim 1 wherein calculating the mood transition
includes using a coordinate system to determine that the quantified
mood transition lies within boundaries of a mood indicator for
content acting as a baseline in determining the quantified mood
transition.
4. The method of claim 1 wherein the mood transition threshold
varies asymmetrically with a mood indicator for content acting as a
baseline in determining the quantified mood transition.
5. The method of claim 4 wherein the asymmetric mood transition
threshold reflects priorities for identifying a track.
6. The method of claim 1 further comprising enabling the user to
access a playlist of the present track, the intermediary tracks,
and the end track.
7. The method of claim 6 wherein enabling the user to access the
playlist includes transmitting the content in the playlist to the
user.
8. The method of claim 1 wherein the mood originating point
indicates a mood of the content the user receives.
9. The method of claim 1 wherein the mood originating point
indicates the mood of the user.
10. The method of claim 1 further comprising enabling the user to
access the intermediary tracks.
11. The method of claim 1 further comprising identifying an updated
mood state for the user to determine if the updated mood state is
compatible with a calculated mood state for the mood
transition.
12. The method of claim 11 wherein identifying the updated mood
state for the user is performed as the user is accessing the
intermediary tracks.
13. The method of claim 11 further comprising selecting a new end
track for the user when the updated mood state is incompatible with
the calculated mood state.
14. The method of claim 11 further comprising selecting one or more
new intermediary tracks when the updated mood state is incompatible
with the calculated mood state.
15. The method of claim 1 wherein determining the mood originating
point for the present track for the user, identifying the mood
destination for the user playlist, and calculating the mood
transition includes determining the mood originating point,
identifying the mood destination, and calculating the mood
transition for an audience of multiple users.
16. The method of claim 14 wherein determining the mood originating
point, identifying the mood destination, and calculating the mood
transition for the audience includes modeling the audience based on
aggregated mood state information from one or more individual users
in the audience.
17. The method of claim 15 wherein modeling the audience includes
modeling the audience as a single user.
18. The method of claim 15 wherein modeling the audience includes
sampling a subset of the audience and using one or more sampled
results to calculate the mood transition.
19. The method of claim 15 wherein modeling the audience includes
modeling the audience as a collection of groups.
20. A content selection system comprising: an origination code
section structured and arranged to determine a mood originating
point for a present track for a user, the mood originating point
being related to a mood indicator for the present track; an
identification code segment structured and arranged to identify a
mood destination for a user playlist, the mood destination being
related to a mood indicator for an end track that is targeted for
the user; and a calculation code segment structured and arranged to
calculate a mood transition from the mood originating point to the
mood destination, the mood transition comprising one or more
intermediary tracks between the mood destination and the mood
originating point, such that a quantified mood transition between
two tracks in user playlist comprising the present track, the
intermediary tracks, and the end track, is less than an identified
mood transition threshold.
21. The system of claim 19 further comprising a mood modeling
engine structured and arranged to determine the mood indicator for
the present track or the end track by accessing a model of user
mood states.
22. The system of claim 19 wherein the calculation code segment is
structured and arranged to use a coordinate system to determine
that the quantified mood transition lies within boundaries of a
mood indicator for content acting as a baseline in determining the
quantified mood transition.
23. The system of claim 19 wherein the mood transition threshold
used varies asymmetrically with a mood indicator for content acting
as a baseline in determining the quantified mood transition.
24. The system of claim 22 wherein the asymmetric mood transition
threshold reflects priorities for identifying a track.
25. The system of claim 19 further comprising a communications
interface structured and arranged to enable the user to access a
playlist of the present track, the intermediary tracks, and the end
track.
26. The system of claim 24 wherein the communications interface is
structured and arranged to transmit the content in the playlist to
the user.
27. The system of claim 19 wherein the mood originating point
indicates a mood of the content that the user receives.
28. The system of claim 19 wherein the mood originating point
indicates the mood of the user.
29. The system of claim 19 further comprising a communications
interface structured and arranged to enable the user to access the
intermediary tracks.
30. The system of claim 19 further comprising an updating code
segment structured and arranged to identify an updated mood state
for the user to determine if the updated mood state is compatible
with a calculated mood state for the mood transition.
31. The system of claim 29 wherein the updating code segment is
structured and arranged to identify the updated mood state for the
user is performed as the user is accessing the intermediary
tracks.
32. The system of claim 29 further comprising an alternate end
track selection code segment structured and arranged to select a
new end track for the user when the updated mood state is
incompatible with the calculated mood state.
33. The system of claim 29 further comprising an alternate
intermediary track code segment structured and arranged to select
one or more new intermediary tracks when the updated mood state is
incompatible with the calculated mood state.
34. The system of claim 19 wherein the origination code segment,
the identification code segment, and the calculation code segment
are structured and arranged to determine the mood originating
point, identify the mood destination, and calculate the mood
transition for an audience of multiple users.
35. The system of claim 19 wherein the origination code segment,
the identification code segment, and the calculation code segment
are structured and arranged to model the audience based on
aggregated mood state information from one or more individual users
in the audience.
36. The system of claim 34 wherein the origination code segment,
the identification code segment, and the calculation code segment
are structured and arranged to model the audience as a single
user.
37. The system of claim 34 wherein the origination code segment,
the identification code segment, and the calculation code segment
are structured and arranged to sample a subset of the audience and
using one or more sampled results to calculate the mood
transition.
38. The system of claim 34 wherein the origination code segment,
the identification code segment, and the calculation code segment
are structured and arranged to model the audience as a collection
of groups.
39. A content selection system comprising: means for determining a
mood originating point for a present track for a user, the mood
originating point being related to a mood indicator for the present
track; means for identifying a mood destination for a user
playlist, the mood destination being related to a mood indicator
for an end track that is targeted for the user; and means for
calculating a mood transition from the mood originating point to
the mood destination, the mood transition comprising one or more
intermediary tracks between the mood destination and the mood
originating point, such that a quantified mood transition between
two tracks in user playlist comprising the present track, the
intermediary tracks, and the end track, is less than an identified
mood transition threshold.
Description
TECHNICAL FIELD
[0001] This document relates to content selection.
BACKGROUND
[0002] Digital content is distributed on a wide variety of devices
and in a wide variety of formats. The digital content may include
movies, music, slides, games and other forms of electronic
content.
SUMMARY
[0003] In one general sense, access to content by a user may be
managed by determining a mood originating point for a present track
for a user, with the mood originating point being related to a mood
indicator for the present track. A mood destination for a user
playlist is also identified, with the mood destination being
related to a mood indicator for an end track that is targeted for
the user. A mood transition from the mood originating point to the
mood destination then is calculated. The mood transition includes
one or more intermediary tracks between the mood destination and
the mood originating point, such that a quantified mood transition
between two tracks in a user playlist that includes the present
track, the intermediary tracks, and the end track, is less than an
identified mood transition threshold.
[0004] Implementations may include one of more of the following
features. For example, the mood indicator for the present track or
the end track may be determined by accessing a model of user mood
states. Calculating the mood transition may include using a
coordinate system to determine that the quantified mood transition
lies within boundaries of a mood indicator for content acting as a
baseline in determining the quantified mood transition. The
identified mood transition threshold may vary asymmetrically with a
mood indicator for content acting as a baseline in determining the
quantified mood transition. The asymmetric mood transition
threshold may reflect priorities for identifying a track.
[0005] The user may be enabled to access a playlist of the present
track, the intermediary tracks, and the end track. Enabling the
user to access the playlist may include transmitting the content in
the playlist to the user. The mood originating point may indicate a
mood of the content the user receives and/or the mood of the
user.
[0006] An updated mood state for the user may be identified to
determine if the updated mood state is compatible with a calculated
mood state for the mood transition. Identifying the updated mood
state for the user may be performed as the user is accessing the
intermediary tracks. A new end track for the user may be selected
when the updated mood state is incompatible with the calculated
mood state. One or more new intermediary tracks may be selected
when the updated mood state is incompatible with the calculated
mood state.
[0007] Determining the mood originating point for the present track
for the user, identifying the mood destination for the user
playlist, and calculating the mood transition may include
determining the mood originating point, identifying the mood
destination, and calculating the mood transition for an audience of
multiple users. Determining the mood originating point, identifying
the mood destination, and calculating the mood transition for the
audience may include modeling the audience based on aggregated mood
state information from one or more individual members of the
audience. Modeling the audience may include modeling the audience
as a single member and/or sampling a subset of the audience and
using one or more sampled results to calculate the mood transition.
Modeling the audience may include modeling the audience as a
collection of groups.
[0008] These and other aspects may be implemented by a system
and/or a computer program stored on a computer readable medium,
such as a disc, a client device, a host device, and/or a propagated
signal. The system may include a host device, a client device, or
componentry distributed on more than one system.
[0009] Other features will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0010] FIG. 1 is a block diagram of a communications system that
enables electronic content to be distributed.
[0011] FIG. 2A is a graph of a mood spectrum that illustrates how a
selection of content may be scored to quantify the mood in an
automated manner.
[0012] FIG. 2B is a graph illustrating how a mood spectrum and
scoring system may be used to associate content with an actual
mood.
[0013] FIG. 2C is a graph illustrating a three-dimensional mood
management system that illustrates how mood consistency may be
maintained using three or more factors.
[0014] FIG. 2D is a graph illustrating how mood transitions may
incorporate intermediate tracks to create a more successful
transition in reaching a mood destination.
[0015] FIG. 3 is a block diagram of a mood-based playlisting
system.
[0016] FIG. 4 is a flow chart showing how mood consistency may be
maintained between two tracks.
[0017] FIG. 5 is a flow chart showing how mood consistency may be
maintained using a three-dimensional model to determine mood
consistency.
[0018] FIG. 6 is a flow chart showing how a playlist of content may
be transitioned from a mood originating point to a mood destination
using intermediate tracks.
DETAILED DESCRIPTION
[0019] The flexibility and power of communications networks and
media software (e.g., streaming audio and video players) enable
wider access to electronic content in addition to enabling new
media products. The organization of content that is transmitted to
a user may be referred to as a playlist. Typically, each playlist
includes a collection of tracks, each of which may have an
associated mood. For example, the associated mood may be a mood
that a song inspires in the listening audience. Alternatively, the
associated mood may indicate the mood of a listener who has
requested the song or the "station." In yet another example, the
mood may be described as a collection of attributes that include,
for example, the tempo (e.g., slow), the theme (e.g., country), and
the tone (e.g., deep male vocal). Regardless of the how the
underlying mood is affiliated with the content, the content may be
arranged so that a mood consistency is maintained between songs.
Generally, a mood consistency relates to the likelihood that a user
will elect to remain in the audience community as one selection of
content ends and a second selection of content begins. For example,
an Internet-based radio station may be using a server to distribute
content. The server may organize the content so that a country rock
song gauged to be uplifting is not interspersed between two country
ballads gauged to be depressing if that sequence is determined to
lose the listening audience. The automated arrangement of tracks
may be referred to as a mood-based playlisting system.
[0020] Although the mood-based playlisting system may be easiest to
understand when considering the operations of an Internet-based
radio station that is selecting songs, the mood-based playlisting
system may be used in a variety of contexts and with diverse
content. Thus, the mood-based playlisting system may be used to
select advertisements (including audio, video, and emerging media),
video programming, and other forms of content (e.g., Web-based
programming).
[0021] If maintaining mood consistency between two tracks appears
to be difficult, the mood-based playlisting system may use one or
more intermediate tracks to increase the likelihood of success
between prior content (also called an originating point) and a
targeted piece of content (the mood of which is referred to as a
mood destination).
[0022] FIG. 1 illustrates a media-based communications system 100
that may distribute content electronically. The media-based
communications system 100 includes a content source 110, a network
120, and a player 130. Although the media-based communications
system 100 is shown as a network-based system, the media-based
playlisting system may access media files residing in a standalone
device or in a different configuration. For example, a mobile
jukebox may play content in the form of music encoded in a media
file format.
[0023] The content source 110 generally includes one or more
devices configured to distribute digital content. For example, as
shown, the content source 110 includes a server 112 and a
duplicating switch 114.
[0024] Typically, a content source 110 includes a collection or
library of content for distribution. Alternatively, or in addition,
the content source may convert a media source (e.g., a video or
audio feed) into a first feed of data units for transmission across
the network 120. The content source 110 may include a
general-purpose computer having a central processor unit (CPU), and
memory/storage devices that store data and various programs such as
an operating system and one or more application programs. Other
examples of a content source 110 include a workstation, a server
112, a special purpose device or component, a broadcast system,
other equipment, or some combination thereof capable of responding
to and executing instructions in a defined manner. The content
source 10 also may include an input/output (I/O) device (e.g.,
video and audio input and conversion capability), and peripheral
equipment such as a communications card or device (e.g., a modem or
a network adapter) for exchanging data with the network 120.
[0025] The content source 110 includes playlisting software
configured to manage the distribution of content. The playlisting
software organizes or enables access to content by a user
community. For example, the content source 110 may be operated by
an Internet radio station that is supporting a user community by
streaming an audio signal, and may arrange a sequence of songs
accessed by the user community.
[0026] The playlisting software includes mood-based playlisting
software that maintains a consistent mood in selecting content.
Generally, the mood-based playlisting software selects content so
that any related mood transition between different content
components is acceptable.
[0027] The content source includes a duplicating switch 114.
Generally, a duplicating switch 114 includes a device that performs
network operations and functions in hardware (e.g., in a chip or
part of chip). In some implementations, the duplicating switch may
include an ASIC ("Application Specific Integrated Circuit")
implementing network operations logic directly on a chip (e.g.,
logical gates fabricated on a silicon wafer and then manufactured
into a chip). For example, an ASIC chip may perform filtering by
receiving a packet, examining the IP address of the received
packet, and filtering based on the IP address by implementing a
logical gate structure in silicon.
[0028] Implementations of the device included in the duplicating
switch may employ a Field Programmable Gate Array (FPGA). A FPGA is
generally defined as including a chip or chips fabricated to allow
a third party designer to implement a variety of logical designs on
the chip. For example, a third party designer may load a FPGA with
a design to replace the received IP addresses with different IP
addresses, or may load the FPGA with a design to segment and
reassemble IP packets as they are modified while being transmitted
through different networks.
[0029] Implementations of the device included in the duplicating
switch also may employ a network processor. A network processor is
generally defined to include a chip or chips that allow software to
specify which network operations will be performed. A network
processor may perform a variety of operations. One example of a
network processor may include several interconnected RISC ("Reduced
Instruction Set Computer") processors fabricated in a network
processor chip. The network processor chip may implement software
to change an IP address of an IP packet on some of the RISC
processors. Other RISC processors in the network processor may
implement software that monitors which terminals are receiving an
IP stream.
[0030] Although various examples of network operations were defined
with respect to the different devices, each of the devices tends to
be programmable and capable of performing the operations of the
other devices. For example, the FPGA device is described as the
device used to replace IP addresses and segment and reassemble
packets. However, a network processor and ASIC are generally
capable of performing the same operations.
[0031] The network 120 may include hardware and/or software capable
of enabling direct or indirect communications between the content
source 110 and the player 130. As such, the network 120 may include
a direct link between the content source and the player, or it may
include one or more networks or subnetworks between them (not
shown). Each network or subnetwork may include, for example, a
wired or wireless data pathway capable of carrying and receiving
data. Examples of the delivery network include the Internet, the
World Wide Web, a WAN ("Wide Area Network"), a LAN ("Local Area
Network"), analog or digital wired and wireless telephone networks,
radio, television, cable, satellite, and/or any other delivery
mechanism for carrying data.
[0032] The player 130 may include one or more devices capable of
accessing content on the content source 110. The player 130 may
include a controller (not shown) that processes instructions
received from or generated by a software application, a program, a
piece of code, a device, a computer, a computer system, or a
combination thereof, which independently or collectively direct
operations of the player 130. The instructions may be embodied
permanently or temporarily in any type of machine, component,
equipment, storage medium, or propagated signal that is capable of
being delivered to the player 130 or that may reside with the
controller at player 130. Player 130 may include a general-purpose
computer (e.g., a personal computer (PC) 132) capable of responding
to and executing instructions in a defined manner, a workstation, a
notebook computer, a PDA ("Personal Digital Assistant") 134, a
wireless phone 136, a component, other equipment, or some
combination of these items that is capable of responding to and
executing instructions.
[0033] In one implementation, the player 130 includes one or more
information retrieval software applications (e.g., a browser, a
mail application, an instant messaging client, an Internet service
provider client, or an AOL TV or other integrated client) capable
of receiving one or more data units. The information retrieval
applications may run on a general-purpose operating system and a
hardware platform that includes a general-purpose processor and
specialized hardware for graphics, communications and/or other
capabilities. In another implementation, player 130 may include a
wireless telephone running a micro-browser application on a reduced
operating system with general purpose and specialized hardware
capable of operating in mobile environments.
[0034] The player 130 may include one or more media applications.
For example, the player 130 may include a software application that
enables the player 130 to receive and display an audio or video
data stream. The media applications may include controls that
enable a user to configure the user's media environment. For
example, if the media application is receiving an Internet radio
station, the media application may include controls that enable the
user to select an Internet radio station, for example, through the
use of "preset" icons indicating the station genre (e.g., country)
or a favorite. In another example, the controls may enable the user
to rewind or fast-forward a received media stream. For example, if
a user does not care for a track on a particular station, the user
may interface with a "next track" control that will queue up
another track (e.g., another song).
[0035] The media application includes mood-based playlisting
software. The mood-based playlisting software may work
independently of, or in conjunction with, playlisting software
residing on the content source 110. The mood-based playlisting
software may mitigate the mood transition created when the content
changes. In one example, the playlisting software permits the user
to select from a recommended list of content that is consistent
with the previous or present track. In another example, the
mood-based playlist software may seamlessly manage the transition
of content.
[0036] FIGS. 2A-2D describe a mood modeling system that may be used
by the systems described with respect to FIG. 1. FIG. 2A
illustrates a mood spectrum 200 that may be used to determine a
mood consistency between a selection of content and planned future
content. Mood spectrum 200 has been abstracted to be independent of
the underlying mood, and has been normalized in the range from 0 to
10. In mood spectrum 200, the mood indicator 205 for the current
track has a value of approximately 5 on the mood spectrum 200. The
mood indicator 205 for the current track is related to the mood
spectrum 210 consistent with the current track, which indicates
mood values for content that may be selected consistent with the
mood value for the current track under consideration. In one
example, the playlist and content selection is being planned and
the current track under consideration has not been distributed. In
another example, the current track under consideration has been or
is being distributed (e.g., across the Internet by an Internet
radio station).
[0037] FIG. 2B illustrates a graph 220 how content may be
categorized using one or more moods and specifically describes how
the mood indicator associated with a particular piece of content
may span multiple moods. As shown, the moods include "angry,"
"excitement," "dance," "romantic," "mellow," and "sad." FIG. 2B
uses a 1-dimensional axis to categorize content along the mood
spectrum 225. Specifically, the content in FIG. 2B spans two of the
moods, specifically, dance and romance. Other dimensioning systems
relating to more than two moods may be used. For example, an X
dimensional system may gauge X moods across X axes. Nevertheless,
regardless of the number of axes that are used, a selection of
content may be related to various moods to identify future content
that is consistent with the mood of the content that has been
selected.
[0038] FIG. 2B includes a mood indicator 230 for the current track.
The mood indicator 230 describes a particular mood rating for a
piece of content that has been identified. The content that has
been identified may include a selection of content that is actually
being played or one that is destined for one or more users.
Alternatively, the mood indicator for a current track may be used
to create a user playlist to better identify desired content deemed
compatible for a user. As is shown in FIG. 2B, the mood indicator
230 for the current track lies within the mood spectrum 225
consistent with the current track. This mood spectrum 225 indicates
that content that falls within dance and romantic themes is deemed
consistent with the mood indicator for the current track.
[0039] In one implementation, the consistency with the current
track and the identification of a particular mood spectrum may be
determined by scoring the current track and a proposed next track
and determining the relationship between the score for the current
track and the score for the proposed next track. Alternatively, a
selection of content may be associated with one or more discrete
values that describe the content. For example, a song may be
associated with letters, each of which describes one or more themes
that may be used to characterize the song. Thus, as is shown in
FIG. 2B, if D and R were used to identify, respectively, dance and
romantic themes, a record describing the current track could have a
D and a R in its record/metadata.
[0040] Referring to FIG. 2C, a three-dimensional mood management
graph 240 is shown that illustrates how mood spectrum consistency
may be determined across three factors, influences, or moods.
Specifically, the three-dimensional coordinate system for the
current track 245 is shown within a three-dimensional volume
describing the mood spectrum boundary 250 as a function of three
coordinates. Also shown is a first song 255 that does not fall
within the volume of the mood spectrum boundaries 250 and a second
song 260 that lies within the mood spectrum boundary 255. Thus,
when content is being selected, if the mood spectrum boundary 250
is being used as the determining criteria, song 255 may be excluded
as it lies outside the mood spectrum boundary 250, while song 260
may be included in the playlist as it lies within the mood spectrum
boundary 250.
[0041] Depending on the implementation and the configuration, the
mood spectrum boundary may represent a simpler function such as a
cone or a sphere. For example, a sphere may be used to identify
equidistant points that fall within a certain mood range of the
current track. However, the mood spectrum boundary 250 need not
include a simple function. For example, if detailed analytics are
used to measure mood spectrum consistency and user response, a more
detailed and non-symmetrical volume may be used to measure the mood
spectrum boundary 250. One illustration of this may include content
that may be very consistent across one axis for multiple themes,
but inconsistent with minor changes across a different axis in mood
spectrum. For example, if the content is being scored across
lyrics, tempo and intensity, lyrics that may contain
age-appropriate suggestions may only be consistent with content
that is similarly appropriate for the identified age. In contrast,
content that features a slower tempo may be consistent with music
across multiple themes with a similar tempo. Accordingly, the
function that describes the mood spectrum boundary 250 of the
current track 240 may incorporate analytics that permit a small
tolerable deviation in the lyrical deviation while also permitting
a wider variation in the tempo axis.
[0042] FIG. 2D illustrates a graph of a three-dimensional mood
consistency scoring system 270 that illustrates how mood
transitions may be planned so that the mood may be changed from a
current mood originating point to a mood destination. The
transitions may be structured such that a transition directly from
a mood originating point to a mood destination that otherwise
appears difficult or unsuccessful may be made more successful by
using one or more intermediate transitions. Thus, the likelihood of
a successful transition between the mood originating point and the
mood destination point is increased.
[0043] Mood scoring system 270 illustrates a mood originating point
275 and a mood destination 280. The general mood transition that is
required is illustrated by the vector 285 from the mood originating
point 275 to the mood destination point 280. However, the mood
consistency volume 277 for mood originating point 275 does not
include the mood destination point 280. Accordingly, one or more
intermediary tracks may be used to successfully transition one or
more users to the mood destination point.
[0044] To accomplish this transition, intermediary track 290 is
used as the next content selection to create a mood that is closer
to the mood destination point 280, even though the consistency
volume 292 for the intermediary track 290 does not actually reach
or include the mood destination 280. After the intermediary track
290 is selected, a second intermediary track 295 is added to the
playlist to move the current mood indicator closer to the mood
destination 280. As is shown in FIG. 2D, the intermediary tracks
290 and 295 both lie within the same transition volume 292, thus
preserving a consistent mood transition from the intermediary track
290 to the intermediary track 295. From the intermediary track 295,
the system may transition directly to the mood destination point
280 and preserve the consistent mood as both the intermediary track
295 and the mood destination point 280 lie within the mood
transition volume 297.
[0045] Although the transition from the mood originating point 275
to the mood destination point 280 features the use of two
intermediary tracks, the implementation of a successful transition
need not be limited to the two intermediary tracks that are shown.
For example, depending on the configuration, no intermediary tracks
may be required to successfully transition from the mood
originating point 275 to the mood destination point 280.
Alternatively, one, two, three, or more intermediary tracks may be
used to successfully transition from the mood originating point 275
to the mood destination point 280.
[0046] The intermediary tracks need not resemble similar forms of
content. For example, the mood originating point for the current
track may include a song that is being transmitted, a first
intermediary track may include a commercial, a second intermediary
track may include a second song, and the mood destination point may
relate to a planned advertisement that has been targeted for
increased chances of success.
[0047] Also, the volumes that describe the mood consistency may be
configured to reflect probabilistic chances of success and may
change, based on the desired chance of success. For example, the
mood consistency volume 277 may be planned on a model of mood
consistency such that transitioning from the mood originating point
275 to the intermediary track 290 will preserve 90% of the audience
upon that transition. Alternatively, if fewer intermediary tracks
are desired, a larger mood consistency volume that covers more
distance may be used based upon a modeled probability of 50%. Thus,
in this model, fewer intermediary tracks may be required to reach
the mood destination point.
[0048] Finally, the transitions that are shown may include
real-time feedback to better predict the actual user response to be
transitioned. For example, a test audience may be sent the
intermediary track in advance of the larger audience. If the
response of the test audience indicates that the transition is not
as successful as was expected, an alternate path may be plotted to
increase the chance that the transition will preserve the audience.
For example, an intermediary track may be chosen that lies closer
to the mood originating point. Another example of an alternative
path that may be chosen includes a trusted transition that has been
used previously and is associated with what is believed to be a
higher success rate in transitioning.
[0049] FIG. 3 illustrates a mood-based playlisting system 300 that
may be used to generate a playlist with consistent moods between
two or more selections. The mood-based playlisting system 300
includes a communications interface 310, a playlist manager 320, a
content library 330, a mood indicator library 340, a mood
calculator 350, and an optional mood-modeling engine 360.
Generally, the mood base playlisting system 300 manages the
playlist for one or more pieces of content to be transmitted to an
audience. The communications interface 310 receives data describing
the audience and one or more content goals to be incorporated, so
that the playlist manager 320 may put together a playlist of
selections from the content library 330 by using the mood indicator
library 340 to determine a score for the content and maintaining
consistency between the selected content using the mood calculator
350.
[0050] The communications interface 310 may be used to exchange
data describing the audience that is being managed and/or to
distribute playlist information. The communication interface 310
also may be used to receive updates to the content library 330, the
mood indicator library 340, and different algorithms and models
used by the mood calculator 350.
[0051] The communications interface 310 receives updates from one
or more partners or other devices to exchange content for
incorporation into a playlist. For example, a newly-released song
may be distributed, along with advertisements for incorporation
into the playlist. Similarly, mood indicator information related to
the content and/or advertising to be distributed also may be
received by the communications interface 310 for transmission to
the mood indicator library 340. Audience data associated with
content may be modeled, described electronically, and transmitted
to the mood calculator 350 to better select content to be
incorporated into the playlist. The playlist manager 320 includes a
code segment that identifies content to be used in a playlist. For
example, the playlist manager 320 may generate a playlist that
describes a piece of content to be accessed and reference
information so that the content may be accessed using the reference
information.
[0052] Alternatively, the playlist manager 320 may generate a
playlist to be used by a distribution point. For example, an
Internet-based radio system may receive the playlist from the
playlist manager for transmission to the listening audience.
Depending on the configuration of the mood-based playlisting system
and whether the mood-based playlisting system is determining the
playlist and distributing the content, the playlist manager 320
also may transmit the content to be used in the playlist (e.g.,
through communications interface 310).
[0053] The content library 330 may include one or more selections
of content for incorporation into a transmission for a receiving
audience. Depending on the nature of the content, the content
library may be adjusted to accommodate the particular media and/or
audio demands. For example, the content library may include
digitally encoded songs and related music videos for broadband
users. The content library also may include metadata that describes
the content. In the case of songs, the metadata may include, for
example, artist, album, and track information. When the content
library includes video information, the video information may
include different bit rates for different audiences. Thus, a user
with a high bandwidth connection may be able to access a selection
encoded for a higher bit rate and having relatively higher quality,
while a user with a slower connection may be able to access the
same content encoded using a lower bit rate and having relatively
lower quality. The content library and the metadata in the content
library also may be associated with one or more rules that may be
used in the content selection. Thus, a particular selection of
content in the content library may have detailed licensing
information that governs how the selection of content may be
accessed. For example, a particular selection of content may be
available for promotional purposes during a limited time and may be
unavailable thereafter. Other examples of restrictions that may be
incorporated in the content library include ASCAP licensing
restrictions that control the number of times a selection or
content may be accessed in a particular period, and preclude a
selection of content from being accessed in a particular manner.
For example, a selection of content may be precluded from being
incorporated in a playlist twice in a row.
[0054] The mood indicator library 340 may include one or more
values designed to describe the mood for a selection of content.
Depending on the configuration of the mood-based playlisting
system, different metrics may be stored in the mood indicator
library 340. Thus, one example of the value stored in the mood
indicator library may describe a selection of content and a mood
indicator that scores the content in a specified numerical range.
Another metric may include different values that indicate whether a
selection of content is compatible with a chosen theme or
genre.
[0055] Although the mood-based playlisting system has been
described as maintaining consistency within a desired mood for a
selection of content, other non-mood-based elements may be modeled
and incorporated into the content selection process and stored in
the mood indicator library. For example, a user pool may be divided
into premium and non-premium communities. The premium community may
be allowed to access exclusive content that is not available to the
non-premium community. This premium status for content that may be
available may be stored in the mood indicator library. Other
non-mood-based metrics may be used.
[0056] The mood calculator 350 may be used to receive values
describing a current playlist, access the mood indicator library
340, and assist the playlist manager 320 in generating the
playlist. Depending on the configuration of the playlist manager
320, the structure of the mood calculator 350 may differ. For
example, in one configuration, the playlist manager 320 may suggest
a particular piece of content and poll the mood calculator 350 to
determine if the selection of content is appropriate and consistent
with the current mood. The mood calculator then may respond with an
indicator of whether the suggested content is consistent.
[0057] Alternatively, the playlist manager 320 may provide an
indicator of a current track that is being transmitted and may poll
the mood calculator 350 for a suggested piece of content. In
response, the mood calculator 350 may poll the mood indicator
library 340 to retrieve a consistent piece of content. The mood
calculator 350 then may transmit the identity of the consistent
content to the playlist manager 320, which may retrieve the content
from the content library 330.
[0058] As an optional element, the mood-based playlisting system
300 may include a mood-modeling engine 360. For example, as content
is being added to the content library 330, the mood-based
playlisting system 300 may interface with the mood-modeling engine
360 to determine and gauge the mood spectrum for the newly-added
content. The mood-modeling engine 360 may use the communications
interface 310 to develop an appropriate mood analytic for the newly
added content. For example, the selected content may be sent to a
testing code segment to determine an anticipated user response.
Alternatively, the mood-modeling engine 360 may interface with the
playlist manager to add the proposed content to a test group of
listeners to gauge their response to the selected content.
[0059] Other analytics that may be used by the mood-modeling engine
360 may include content analysis that may evaluate lyrics, the
tempo, or other elements relating to the content. In one example,
the tempo for a newly-received piece of content may be "scored"
using a frequency analyzer to determine the theme and mood with
which the content is consistent.
[0060] Although the mood-based playlisting system 300 is shown as
an interconnected group of sub-systems, the configuration of the
mood-based playlisting system 300 may include elements that have
allocated the functionality in a different manner. For example, the
content library 330 may be co-located or merged with the mood
indicator library 340. Thus, the mood indicator for a selection of
content may be stored as an element of metadata residing with the
content record. Alternatively, the elements described in mood-based
playlisting system 300 may reside in a larger code segment with
constituent code segments described by the elements shown in FIG.
3.
[0061] FIG. 4 is a flow chart 400 that illustrates how a track of
content may be selected in a way that maintains mood consistency.
Specifically, the flow chart 400 may be implemented using the
mood-based playlisting system such as was described previously. In
general, a mood-based playlisting system determines a mood
indicator that indicates a present mood state of a user (step 410),
determines a mood indicator describing a next track mood spectrum
that is consistent with the mood indicator for the current track
(step 420) and selects a next track that lies within the next track
spectrum for the current track (step 430).
[0062] Initially, the mood-based playlisting system determines a
mood indicator that indicates a present mood state of a user (step
410). Typically, this will include creating a score that describes
the track of content under analysis. For example, a song being
distributed on the radio could be given a score from 0 to 10. In a
multi-dimensional scoring system, the mood indicator could include
a multi-dimensional coordinate that describes the mood indicator
with regard to several variables.
[0063] The mood indicator may be determined in advance of
distributing the track. For example, the system may assemble a user
playlist with a sequence of tracks for distribution. This sequence
may be distributed to distribution nodes (e.g., local radio
stations or regional Internet servers). Alternatively, the mood
indicator may be determined for a track that is being or has been
distributed. For example, the mood indicator may be determined for
a song that is being played over the airwaves.
[0064] A mood spectrum may be determined for the track for which a
mood indicator has just been determined (step 420). The mood
spectrum may be used to select the next track such that the next
track lies within the boundaries of the mood spectrum. As has been
described previously, the mood spectrum may include multiple
variables and may relate to a likelihood of success that a user may
stay with the current distribution (e.g., the same channel) upon
the playing of the next content selection.
[0065] With the mood indicator and the mood spectrum for the
current track determined, a next track is selected that lies within
the mood spectrum (step 430). In one implementation, the next track
may be selected by finding the track that is closest to the current
track. For example, if the current track has a score of 5.17, the
next closest track that may be selected may have a score of
5.18.
[0066] Alternatively, a content programmer may wish to have some
variation within a mood spectrum, and the selection criteria may
include a requirement that the next song differ by more than a
specified variation threshold while still being within the
specified mood spectrum. In the previous example, the content could
be selected to be at least 0.5 units away from the current
selection of 5.17 but still lies within the variation describing
the mood spectrum of 1.0.
[0067] Within the range of values that are acceptable, the content
may be selected randomly or the content may be selected based on
identifying content that matches the criteria (e.g., is the
furthest or closest away within the spectrum). If there is not a
track that lies within the mood spectrum, the mood-based
playlisting system 300 may alter its configuration to generate a
selection of content. For example, the mood spectrum may be
expanded so that more content lies within the mood spectrum. This
may be accomplished by, for example, decreasing the threshold
percentage of a success that is required in the transition or
increasing the values that define the threshold for success. For
example, if the mood spectrum only covered 70's rock, the mood
spectrum may be expanded to include 70's and 80's rock.
[0068] FIG. 5 illustrates a flow chart 500 showing a mood-based
playlisting system that incorporates a three-dimensional mood-based
modeling system. In general, the three-dimensional mood-based
playlisting system operates by determining a coordinate mood
location for a current track that is being played. This may include
or be described as the present mood state of a user. With the
coordinate mood location determined, a compatible mood volume may
be determined that describes future content selections that are
deemed consistent with the present mood state. With the compatible
mood volume identified, one or more tracks that lie within the
compatible mood volume may be identified and a user may be able to
access the identified tracks.
[0069] Initially, a coordinate mood location that indicates the
present mood state of a content selection is determined (step 510).
For example, the mood state may be described on X, Y and Z axes. In
one example, the coordinate mood location is described in the
context of the content that is being distributed. For example, the
mood coordinates may measure the songs lyrics, tempo, and/or style.
Alternatively, the coordinate mood location may also measure or
describe the mood of the audience. For example, a particular song
may be associated with a human emotion such as sadness, joy,
excitement, or happiness. These human emotions may be measured
independent of the underlying theme of the music. For example, some
music whose theme is described as "golden oldies" may be associated
with sadness while other music may be associated with joy.
[0070] With the coordinate mood location determined, a compatible
mood volume describing compatible and consistent future content may
be determined (step 520). For example, a sphere around a coordinate
mood location may be identified that describes content compatible
with the present track. With the compatible mood volume described,
one or more tracks that lie within the mood volume may be
identified (step 530). With the track identified, a user may be
enabled to access the identified track (step 540).
[0071] In FIG. 6, flow chart 600 illustrates how an audience may be
transitioned from an originating piece of content to a destination
piece of content. This may be used, for example, to transition a
user from a particular piece of programming (i.e., the originating
content) to a targeted advertisement (i.e., the destination
content) by tailoring the transitions from the originating content
to the destination content. Accordingly, the likelihood of success
and the effectiveness of the transition may be pursued.
[0072] Generally, the operations described in flow chart 600 may be
performed using the systems and models described with respect to
FIGS. 1-3. For example, the mood-based playlisting system 300 may
be used to generate the playlist that transitions the user from the
originating piece of content to the destination. Similarly, the
transition and intermediate tracks described in FIG. 2D may be used
to increase the effectiveness of the transitions. However,
depending on the characteristics of the originating and destination
content, the selection of the mood-based transition path may
differ.
[0073] Generally, a mood-based playlisting system identifies a mood
destination for a user playlist. A mood originating point is
determined. With the originating point and destination paths known,
a mood transition may be calculated from the mood originating point
to the mood destination.
[0074] Initially, a mood destination for a user playlist is
identified (step 610). Generally, identifying a mood destination
includes identifying a selection of content to be included in the
user playlist. For example, a distributor may wish to place a
certain advertisement. Alternatively, a system administrator may
wish to have an optimal lead-in for a particular piece of
programming for the purpose of, for example, achieving optimal
ratings for network content. This content to be inserted in the
user playlist has an associated mood that relates to the content
being distributed. In yet another example, a system administrator
for a mood-based playlisting system may wish to have an optimal
lead-in to increase the effectiveness and response of the audience
to identified content that is to be transmitted in the future.
[0075] Separately or in parallel, a mood originating point may be
determined (step 620). Determining a mood originating point may
include identifying content that is being distributed or will be
distributed to an audience prior to the transmission of the content
associated with the mood destination. A mood originating point may
be determined for the content that is being distributed. If the
mood originating point differs from the mood destination of the
content being transmitted (or to be transmitted), the resulting
differential may create a mood transition that may create a less
responsive result due to differences in the moods of the particular
content. The mood transition from the mood originating point to the
mood destination is calculated (step 630). Depending on the
variation between the mood destination and the mood originating
point, the transition may include one or more intermediary tracks.
The intermediary tracks may be selected so that the mood metric for
the intermediary tracks lies within the mood-consistency spectrum
or volume of the previous track. Using the previous content or
track as a baseline, the next content or track may be selected to
minimize the number of intermediary tracks between the originating
content and the destination content.
[0076] Other implementations are within the scope of the following
claims. For example, although the mood-based playlisting system has
been described in the context of a distributed system that may
support multiple devices, the mood-based playlisting system may be
distributed across multiple systems and/or reside at a client
device. One example of the mood-based playlisting system being
distributed across multiple devices may include having a portion of
the mood-based playlisting system that operates in a data center
where the content library and mood indicator library reside. These
data center systems may interface with software that operates a
mood calculator and content retrieval program to retrieve the
content library from the central systems.
[0077] Alternatively, the mood-based playlisting system may be more
client-focused and may perform more operations on the client. For
example, the mood-based playlisting system described in FIG. 3 may
be implemented on a personal audio system. The personal audio
system may store multiple selections of content in memory and
generate the playlist that maintains the mood of the content that
has been stored. Alternatively, the mood-based playlisting system
may include a network-based device that implements the content
selection and playlisting on the client device but retrieves
content from a network-based content library.
[0078] The mood-based playlisting system may be configured to
preserve some measure of variation within the playlist. Thus, the
mood-based playlisting system may be configured to recognize that
if three country ballads having moods that have been gauged as
depressing are played, the playlist should then select a country
song having a mood that has been gauged as uplifting. These
variation rules may be described digitally and distributed as
programming criteria alongside or in conjunction with other
licensing restrictions. For example, a license may govern the
frequency with which an artist or song may be played. In addition
to the frequency licensing restrictions, the content distributor
may distribute a mood-based playlisting rule set along with an
electronic library to regulate access to the content.
[0079] Although the mood has been described in the context of
content being played, other techniques may be used to infer the
mood. For example, the client device may monitor how the user
interfaces with the media player. Monitoring a volume level,
monitoring changes to the volume level, and monitoring whether a
user changes an Internet radio station are examples of operations
that may be used to infer the mood. For example, when a media
player detects that a user reduces the volume level when a new
track begins, the media player may determine that the user is
experiencing a less intense mood. In contrast, when the user
increases the volume, the media player may determine that the
user's mood intensifies.
[0080] The user interaction with the media player also may be
analyzed with respect to the content that is accessed. For example,
if the user skips to the next track immediately after accessing a
new track, the media player may determine that the user's mood does
not like the skipped track. The user's action may be extrapolated
so that a mood that is the inverse of the mood of the rejected
content is inferred. To illustrate, a user may initially select a
country music Internet Radio station. The sequence of content
transmitted to the user may include a country rock song, followed
by a country ballad, followed by a country rock song. When the user
listens to the first country rock song, skips the country ballad,
and listens to the second country rock song, the media player (or
host) may determine that the user's mood reflects a preference for
country rock.
[0081] Although the description of a mood indication made
distinctions between the style and genre, the mood indications also
may be made with respect to other factors, including the artist,
the tempo, the era in which the content originated, the album,
and/or other categorization. For other forms of media (e.g., video
or data), the mood indications may include moods related to the
identity of the producer, director, actors, and/or content rating
(child, teen, all-ages) in addition to the category of the
programming.
[0082] Analyzing the user's interactions to determine the mood is
not limited to the user's interaction with a media player. A user's
interaction with an Instant Messaging program, an electronic mail
program, or an Internet Web browser are examples of other user
activities that may be used to determine the mood. Thus, when a
user is typing quickly and exchanging messages with many other
users, an intense mood may be inferred. In contrast, when the user
is determined to be reading web pages at a slower pace, a relaxed
mood may be inferred. The content in the user interaction also may
be used in determining the mood. Thus, the content appearing in a
web page accessed by the user may be used to determine the mood for
the user.
[0083] Although many of the previously described examples link a
certain activity or type of content with a certain mood, the
examples illustrate just one mood that can be associated with an
activity. Other moods may be associated with the activity or type
of content. A selection of content or a user activity also may be
associated with multiple moods. An example of content with multiple
moods may include a song with an uplifting melody and depressing
lyrics. A mood-based playlisting system may use either or both
moods in selecting future content. If the mood-based playlisting
system sought to place an advertisement/product with the uplifting
mood indication, the mood-based playlisting system may incorporate
the uplifting mood in the transition. If the mood-based playlisting
system did not have an intended mood destination in mind, the
mood-based playlisting system may continue to select content with
multiple mood elements to allow for an easier transition to a wider
variety of content. A larger mood volume may represent the multiple
elements with greater dimensions across multiple axes.
[0084] Although the mood-based playlisting system has been
described using playlists, the mood-based playlisting system need
not assemble an actual playlist of songs. Rather, the content
selection may be made on a selection-by-selection basis. The list
of songs selected in this manner may form a playlist.
[0085] Although the mood-based playlisting system has been
described in the context of determining the mood state for a user,
the mood-based playlisting system may be used to determine a mood
state and select content for a group of users. This may include
selecting content for large Internet audiences. For example, the
individual mood states for individual members of a large audience
may be aggregated to determine a collective mood state for the
large audience.
[0086] In one example, the determining collective mood state may
include sampling individual members of the audience for their mood
states and using the sampled mood information to generate a
collective mood state. In another example, an audience listening to
one content source may be analyzed as a collection of groups. The
mood-based playlisting system may analyze each individual group to
determine whether the mood state of the group is consistent with
the content being selected. When the mood state for one of the
groups indicates that the mood state for the group is not
compatible with the mood state for a content selection, the
mood-based playlisting system may reconfigure the selection of
content. In one example, the group experiencing the mood state
incompatibility may be transitioned to a different stream/playlist
to preserve the mood state compatibility. In another example, the
mood-based playlisting system may select different content designed
to retain the group experiencing the mood state incompatibility.
This may include determining that more users are likely to be
retained from the group experiencing the mood state incompatibility
than are lost from other groups not experiencing the mood state
incompatibility.
[0087] The mood-based playlisting system may disperse and group
users. Users may be grouped to reduce costs, to take advantage of
discounts for larger audiences, and to allow a limited pool of
content to serve a larger community. This may include transmitting
the same advertisement or segment lead to multiple users. The
mood-based playlisting system also may disperse users from a common
group. For example, a group of users may be accessing a host to
access a popular selection of content. The mood-based playlisting
system then may personalize the content based on the determined
mood so that the users are retained at a higher rate using the
mood-based playlisting system.
[0088] The mood-based playlisting system may normalize a mood
indication to a designated location or region. The normalization
may be done irrespective of whether targeted content is designated
for the user. For example, the mood-based playlisting system may
determine that operating a playlist in a certain mood spectrum or
volume retains listeners at a greater rate. In another example, the
mood indication for the user is operated in a specified range so
that the user may be more receptive to communications delivered
through other channels. This may include, for example, an
advertisement on television, an electronic mail message, a
telephone call, a Web-based advertisement, or an instant message.
In yet another example, an advertiser may want a certain mood to be
associated with a product. For example, a marketing firm may want a
`happy` mood associated with the firm's content.
[0089] When calculating a mood transition, the mood-based
playlisting system may reexamine the actual mood state during the
transition and determine if the actual mood state matches the
intended mood state. For example, although the mood state of the
content may indicate that a user should be in a relaxed mood, the
user's activities on their client may indicate that the user's mood
state is not mellow (e.g., the user is experiencing stress or
anxiety). The mood-based playlisting system may dynamically respond
to the actual mood state. In one example, the mood-based
playlisting system may select content associated with a different
mood destination that is more compatible with the user's actual
mood state. Thus, instead of playing an advertisement associated
with a mellow mood, the mood-based playlisting system may select an
advertisement with a mood that is compatible with the actual mood
of the user.
[0090] The mood based-playlisting system may include a detailed
records system for reporting and accounting. For example, the
mood-based playlisting system may record the moods of the user, the
mood transition between tracks, and the percentage of users that
are retained for the transition. Other records may include
advertising effectiveness based on the mood, and user listening
habits (e.g., duration, user preferences). The records may be
refined in an automated manner to develop mood trending
information. The mood-based playlisting system may generate
automated reports for system administrators and advertisers to
improve the enjoyment, effectiveness, and/or success of the
mood-based playlisting system. This may include a report indicating
that a different programming sequence may result in an increased
response rate to an advertisement.
[0091] The mood-based reporting system may transmit several
different sequences of content to determine the relative efficacy
of the different sequences. The mood-based reporting system then
may present the results to a system administrator and enable the
system administrator to control future content selection using the
reported relative efficacy information. The reporting system may
present results using different mood metrics. For example, a first
report may be based on only the mood of the content while a second
report may gauge the user interaction with the media player. The
reporting system then may analyze the differences, and interpret
the variation. The interpretation of the variation then may be used
by a system administrator to plan future programming.
* * * * *